Description: Data adjustment is a fundamental process in the field of machine learning that involves preparing and modifying data to optimize the training of predictive models. This process includes various stages, such as data cleaning, where errors and outliers are removed or corrected; data transformation, which may include normalization or standardization of variables; and feature selection, which involves identifying the most relevant variables for the model. The primary goal of data adjustment is to ensure that the data is of high quality and in a suitable format for the machine learning algorithm being used. A well-adjusted dataset not only improves the model’s accuracy but also reduces the risk of overfitting, where the model becomes too tailored to the training data and loses generalization capability. In the context of AutoML, where the aim is to automate the model creation process, data adjustment becomes a critical task, as the quality of the data directly influences the performance of the final model. Therefore, data adjustment is an essential stage that can determine the success or failure of a machine learning project.
